Mass Mahjong Decision System Based on Transfer Learning
Authors: 
Yajun Zheng, Shuqin LiAuthors Info & Claims
Yajun Zheng, Shuqin LiAuthors Info & ClaimsPages 228 - 233
Abstract
In this paper, we propose a transfer learning to solve the problem of lacking in data and the difficulty in constructing models effectively, which is typically represented by Mass Mahjong in the field of imperfect information. Design and implement the Mass Mahjong Discard model based on transfer learning. The previously well-trained Blood Mahjong Discard model on a large dataset is migrated to Mass Mahjong Discard model in a similar domain. In the subsequent model optimization, a self-play based approaching is used to improve the Mass Mahjong Discard model. The experimental results show that the transfer learning-based Mass Mahjong Discard model performs well in the situation of less data, and can fit the Mass Mahjong Discard rule. And the model won the second prize in the Mass Mahjong event of the National University Computer Gaming Competition in 2021.
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Published In
March 2022
240 pages
ISBN:9781450395502
DOI:10.1145/3529466
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 June 2022
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- Research-article
- Research
- Refereed limited
Funding Sources
- Construction Project of computer technology specialty
- Normal projects of General Science and Technology research program
- Normal projects of promoting graduated education program at Beijing Information Science and Technology University
Conference
ICIAI 2022
ICIAI 2022: 2022 the 6th International Conference on Innovation in Artificial Intelligence
March 4 - 6, 2022
Guangzhou, China
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